Thereafter, machine unlearning for different ML models have been explored.
Ginart et al. [1]} studied the problem of data removal for variants of k-means clustering, but cannot be applied to supervised learning.
The works [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]} introduced in Section REF  focus on supervised linear classification models, such as logistic regression, but not specializing for more complex models, such as neural networks.
There are other approximate unlearning methods that fit for specific ML models, such as Brophy et al. [11]} for random forest models, or Nguyen et al. [12]} for bayesian learning.
Bourtoule et al. [13]} proposed a more general algorithm named SISA.
The main idea of SISA is to split the training data into several disjoint shards, with each shard training one sub-model.
To remove a specific sample, the algorithm only needs to retrain the sub-model that contains this sample.
Nevertheless, existing machine unlearning works focus on ML models in traditional centralized settings, i.e., the training data assumed to be globally accessed, which is ineligible for unlearning in the FL settings.
Liu et al. [14]} studied client-level unlearning in FL, i.e., to eliminate the influence of data of a FL device on the global model, and proposed an algorithm named FedEraser.
The main idea of FedEraser is to unlearn model by leveraging the historical parameter updates of clients that have been retained at the federated server during the training process.
However, for convolutional architectures, [14]} show that unlearning is possible for only shallow networks, such as for a 2-layer CNN followed by 2 fully-connected (FC) layers.
How much the amount of stored information in the federated server also leads to a trade-off between the accuracy and scalability.
